Privacy Preserving Parallel Distributed Data Stream Anonymization

Sustainable stream processing algorithms have gained popularity in recent years. Flow control is a way of searching and modifying real-time data streams. Missing values are ubiquitous real-world streams, making privacy challenging to safeguard. On the other hand, most preservation methods need not take absent into account when developed. They can anonymize certain study, however this results loss. This research proposes unique parallel distributed approach for protecting while using incomplete method uses production computational system continually clustering construct each tuple. It clusters partial complete forms variable array dimensions as similarity metrics. In order prevent outliers’ pollution, generalization that based on more than matches used. The experiments used real compare current systems with varied settings. will cover several anonymization mechanisms their advantages. There also drawbacks. Finally, we explore future continuous research.